As described above, intra-domain discrepancy causes a distribution gap between sub-distributions of the target domain. In the context of SSDA intra-domain discrepancy occurs when the supervision of the samples from the source and target domains enforces the trained model to partially align source and target examples, thus leading to the presence of two unaligned sub-distributions corresponding to labeled and unlabeled data in the target domain.
Following Olson et al. [1]}, [2]}, we propose an intra-domain mixing scheme using a student-teacher network to alleviate the intra-domain gap between sub-distributions within the target domain via category-level mixing that is applied to unlabeled data in the target domain. We feed an input image pair to the teacher network and a mixed image to the student network. We then enforce matching of blended input student predictions to blended teacher predictions.
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